Environmental Data, Modeling and Digital Simulation for the Evaluation of Climate Adaptation and Mitigation Strategies in the Urban Environment

By inergency On Mar 6, 2024

This article deals with the construction of a methodology, based on a knowledge approach, to evaluate the capacity of adaptation and mitigation of the building–outdoor space system to climate change through the measurement of the performances of climate-resilient meta-design proposals. The field of experimentation is the urban district of Piscinola-Miano in the city of Naples.

2.3. Knowledge-Based Approach to Environmental Systems in Urban Settlements

Knowledge of environmental systems in urban settlements is a complex challenge, as it involves several elements, including morphological and spatial interactions. To address this complexity, it is essential to use predictive models of knowledge to develop strategies and decision support tools aimed at identifying technical and design solutions that can improve the adaptation and mitigation capabilities of urban settlements in the face of climate change [16]. In the context of this research, a knowledge-based approach was implemented to address the knowledge of the environmental system in urban settlements. This approach aims to promote site-specific design experiments for the urban districts, to develop innovative solutions for adapting to climate impacts and mitigating GHGs emissions. The knowledge-based approach is based on the collection and analysis of detailed data on the study area, including environmental, morphological, and spatial characteristics, as well as information on social and economic dynamics. This knowledge provides a solid basis for the design and implementation of project interventions that take into account the main characteristics of the urban environment. The goal is to contribute to tackling the impacts of climate change while also acting on GHG emission reduction at the building and urban scale, through design solutions that take into account the specific needs of the area.

The approach is based on the National Plan for Adaptation to Climate Change (NPACC) of the Ministry of Environment and Security [17], which identifies strategies and actions (“soft” actions—non-infrastructural; “grey” actions—infrastructural; “green” actions”—infrastructural linked to ecological aspects) aimed at mitigating climate risks and enhancing the resilience of environmental systems. The plan provides important guidance on how to read environmental systems to define appropriate lines of development to tackle climate change. To this end, the knowledge model tested in the study area proposes an integration of the green and grey levels to describe urban conditions and the interaction between the natural and anthropic environment in a systemic manner. The adopted approach analyzes the environmental system by structuring it into five sectoral areas and layers [18]: non-structural aspects (SOFT layer); infrastructural aspects related to transport and technological networks (GREY layer); aspects related to the green system (GREEN layer); aspects related to the water system (BLUE layer); and built-up system aspects (RED layer) (Figure 3).

Construction of the GIS-Based Database

The described knowledge model was implemented through the construction of a GIS-based database. The opensource software QGIS 3.28.15 “Firenze” was used to construct the database. In the first step, all the relevant vectorial layers were imported into the GIS environment, each with specific features representing the different morphological, environmental, and functional-spatial characteristics of the study area, to support the analysis of the environmental system according to the different sectoral categories (see Section 2.4.1). For each sectoral area of analysis, a corresponding thematic area was developed. For example, for the green system, all the polygons representing the different types of green areas in the area under examination were identified and mapped. Subsequently, each polygon was associated with the category of green area (such as natural, rural, infrastructural, urban, and pertaining green areas), and distinct symbols were assigned to each category. In addition, a category classification was carried out to make the differences between different types of green areas clearer. Finally, using the ‘field calculator’ and the ‘calculate geometry’ command, the area in square meters of each polygon was calculated to quantify the percentage incidence of each category identified in the study area. This type of analysis, based on satellite data (Google, ESRI) and official databases from the administrations (Topographical Database and CTR), made it possible to obtain information on the spatial and quantitative distribution of natural capital in the study area. The same thematic process was applied to the other physical and structural sector areas (green, grey, red, blue).

Moreover, the database has been implemented with the addition of features related to a set of environmental indicators (albedo and surface runoff coefficient). Using the same cartographic base, in a GIS environment, the polygons representing the building–open space system were associated with the corresponding values of the identified indicators. This integration is aimed at assessing the problems of the area concerning climate impacts.

2.4. The Operative Protocol to Test Climate Adaptation and Mitigation Solutions’ Effectiveness

The operative protocol represents the second phase of the proposed methodological workflow to identify the most critical urban areas to climate impacts and select the appropriate climate resilient design solutions in urban settlements.

In the first step, the climate-resilient design solutions are chosen in accordance with the environmental system analyses carried out in the first phase of the methodological workflow. Afterwards, an indicators and indexes system is defined for measurement of the microclimatic behavior of the building–outdoor space system. The next steps are modeling, simulation, and data extraction. Both modeling and simulation processes for the analysis of the microclimatic and performance behavior of the building–outdoor space system, after the implementation of the categories of intervention, are based on an operative workflow for data exchange using different ICT tools [19]. The operative protocol proposed in this paper (Figure 4) is based on the use of Grasshopper’s Virtual Programming Language (VPL) platform and some of its add-ons, such as Ladybug and Dragonfly. The use of Grasshopper’s VPL allowed the modeling capabilities of the three-dimensional modeling software Rhinoceros 6 to be combined with the analytical potential of the microclimate modeling software ENVI-met 5. In this way, it is possible to set a process for testing the environmental performance of the design proposal to assess the improvement of the capacity to adapt to climate impacts and reduce the CO₂ concentration.

The geometric modeling process was conducted through Rhinoceros 6 software. Meanwhile, the microclimatic simulation process was conducted through ENVI-met 5. The use of parametric design tools such as Grasshopper, Dragonfly, and df_envimet allowed the association of the simulation of the physical behavior of the elements within the urban area with the three-dimensional model, thus making it possible to assess the interactions between these elements and the surrounding environmental components.

2.4.1. Identification of Climate-Resilient Technical Solutions

To provide the responses to climate adaptation and mitigation goals in the study case, it was necessary to identify adequate climate-resilient technical solutions at different scales. Based on the analyses at the urban scale carried out in the first phase, the following strategies have been developed at the district scale (Piscinola-Miano and Scampia) (Figure 5):

The introduction of ecological mobility through new cycle paths and the implementation of existing pedestrian paths; energy self-generation with the inclusion of plants for the production of energy from renewable sources;
The increase in urban greening by redeveloping the spaces and by implementing links between different green areas within the study area; improvement of the water management system of the area through the insertion of systems for the collection and reuse of rainwater and reduction of the degree of waterproofing of the area;
The improvement of the quality of existing building stock, mainly through the upgrading of public housing buildings.

To obtain measurable results, it was necessary to make a shift from the district and strategic scale to the more operative one of the building–system outdoor space, where these strategic lines have been detailed focusing on three main aspects: water-sensitive management, sustainable management of green areas, and self-production of energy from renewable sources (Figure 5).

The technical solutions to improve the adaptation and climate mitigation of the building–outdoor space system derives from the study and analysis of similar experiences at the national and international levels, such as the Urban Adaptation Support Tool [20], developed within the European Platform for Climate Adaptation Adapt [21], and the Urban Green-Blue grids for resilient cities [22]. The solutions identified by the analysis of the catalogues are included in broader categories of intervention for climate-resilient design, focusing on climate phenomena linked to the increase in temperatures and rainfall.

Regarding outdoor space, the categories of climate-resilient intervention that have been used within the experimental application presented in this work are the following:

Greening—the inclusion of elements such as trees, rain gardens, or small green areas; this type of solution helps to reduce the concentration of CO₂ in urban areas while improving, at the same time, the conditions of outdoor thermo-hygrometric comfort, by creating shady areas and activating evapotranspiration phenomena, with positive effects on the reduction of urban temperatures and the impacts of heat waves and the urban heat island effect [23]; in addition, solutions such as wetlands, bioshields, buffer zones, green roofing, tree pits, and street side swales contribute to the reduction of the impacts of urban flooding by controlling the surface runoff;
De-paving—reducing the level of waterproofing of horizontal urban surfaces by introducing permeable pavements, with adequate thermal and physical capacities that allow urban surfaces to not reach high temperatures and to contribute to the surface outflow of rainwater, thanks to the permeability of the materials concerning the underlying layers [24];
Cool materials, characterized by high solar reflectance—the use of this type of materials within urban contexts promotes the reduction of surface temperatures and contributes to a reduction of the urban heat island effect [25,26].

Because of the lack of data and information on the building, which is not accessible, and due to the authorship of architect Gerardo Mazziotti, which imposes several constraints, a smaller number of design interventions have been proposed:

Extensive green roof to limit heat loss, generating a positive impact on the energy needs of the building—in addition, the presence of vegetation on the roof promotes the absorption of CO₂ from the surrounding environment and increases the coverage’s ability to reflect solar radiation [27];
Replacement of fixtures with high-performance fixtures to reduce the thermal dispersion of the building—this leads to a significant reduction in energy consumption and, consequently, of the GHGs into the atmosphere;
Photovoltaic system—a system that uses solar energy to produce electricity without CO₂ emissions or other pollutants, thus contributing significantly to mitigating the environmental impact of the building through the activation of a system for the self-production of energy from renewable sources.

The proposed interventions for the building focused mainly on the envelope and the control of heat loss, allowing for optimization of the energy efficiency of the complex and consequently reducing CO₂ emissions [27].

2.4.2. Definition of a System of Indices and Indicators for the Analysis of the Microclimatic Behavior of the Building–Outdoor Space System

To evaluate the adaptation capacity to extreme climatic events, such as heat waves in urban areas, and the ability to decrease GHG emissions, a set of indices and indicators was established. This set, derived from the scientific literature and the current state of the art, was integrated in the operative workflow. In this way, it was possible to estimate the performances of proposed climate-proof design categories (see Section 2.4.1) by using computational tools to evaluate the microclimate behavior of the case study area.

The indices and indicators chosen are as follows:

PMV—predicted mean vote;
Tmrt—mean radiant temperature (measured in °C);
PoT—potential air temperature (measured in °C);
TSur—surface temperature (measured in °C);
CO₂ concentration (measured in ppm—parts per million).

The PMV is an index to assess the thermal comfort perceived by users. This index takes into account both environmental and subjective variables. The result of the index is a numerical value on a scale that typically ranges from -3 (very cold) to +3 (very hot), with 0 representing the state of thermal comfort. However, since it is generally an index derived from a mathematical function for the restitution of indoor thermal comfort, the lower and upper limits of the experimental values provided by the Fanger scale are not necessarily exhaustive for outdoor conditions and may reach values even outside the proposed standard scale [28,29].

In setting the parameters for calculating the PMV, those relating to the characteristics of the subject with respect to whom the calculation is made play an important role [28,29]. The types of subjects taken into account in the experimental application for the former Centro Polifunzionale Marianella are:

Adult (man, height 175 cm, weight 75 kg, 35 years of age, clothing value 0.70);
Senior, representative of the weak groups that will occupy the building according to the project hypothesis (man, height 165 cm, weight 65 kg, 75 years of age, clothing value 0.70).

Mean radiant temperature is a synthetic indicator useful for assessing the impact of heat on the human body and thermo-hygrometric outdoor comfort [30,31,32] and is considered one of the most suitable indicators for assessing the impact of extreme heat events on humans due to its close relationship with urban morphology and vegetation characteristics.

Potential air temperature is the temperature an air mass would have if it were brought to a standard pressure. Surface temperature, on the other hand, gives the temperature of the air near the earth’s surface. Both are expressed in °C.

These four parameters are used to evaluate the adaptation capacity to climate impacts of the complex ex-ante and ex-post the proposed project intervention. On the other hand, regarding the evaluation of climate mitigation capacity, it was chosen to measure and evaluate the CO₂ concentration values that climate-responsive interventions will be able to guarantee in absolute terms [33]. The value returned by the ENVI-met 5 software is in parts per million (ppm), which indicates that for every million parts of air, one part is carbon dioxide.

2.4.3. Modeling Phase

The first stage of the modeling phase involved the three-dimensional geometric modeling of the study area under analysis. This process was conducted using the 3D modeling software Rhinoceros 6. The process involves modeling the area, the object of analysis, from two-dimensional graphs (plans and sections). There are, therefore, some necessary details to be taken into account to carry out the next microclimate simulation phases. Firstly, when developing the model, it is necessary to ensure that all the geometries formed are closed surfaces and solids that are subsequently read by Grasshopper as “closed brep”. This is necessary in order to avoid reading and transfer problems from the three-dimensional Rhinoceros environment to the parametric Grasshopper environment.

As part of the experimentation, the three-dimensional geometric model of the study area was simplified down to the restitution of just the volume as far as the building is concerned. This decision is due to the desire to speed up the following simulation process, simplifying the restitution of the real condition in the digital environment as much as possible. Moreover, as the area is located in the plain north of Naples with minimal differences in elevation, it was decided to represent all the surfaces of the outdoor space on a single plane, providing the 0.0 elevation of the elaborated model.

In the geometric modeling phase, however, it is necessary to make an initial comparison with the possibilities of the microclimate modeling software ENVI-met 5. As part of the developed workflow, it was decided to use the open-source version of the software, which imposes limits on the size of the area that can be simulated from time to time [34]. This restriction requires that the model of the area whose microclimatic behavior is to be modeled and simulated be inscribed in a parallelepiped of dimensions 50 × 50 × 40 cells. For this purpose, a correction had to be made to the modeling phase. This correction consists of developing two different models with two different degrees of resolution, which will subsequently also lead to a double data collection campaign in the simulation phase.

The first model that represents the entire study area is simplified not only in terms of digital transfer of the building but also of outdoor space. This is because, in order to fit the entire area into the maximum simulable model size provided by the software, it is necessary to apply a data resolution of 10:1 type (which means that the information of 10 cells is synthesized in a single cell). This resolution entails the necessary simplification or elimination of all those elements whose size is too small to be read by the software and which would therefore complicate the transition from the three-dimensional model to the microclimate model.

The second model, on the other hand, retains all the complexity of the outdoor space while remaining at the volumetric level as far as the building is concerned. To do this, it was necessary to divide the study area into 27 quadrants of dimensions 50 × 50 m so that they would fall within the permissible dimensions provided by ENVI-met (50 × 50 × 40). In this case, it was decided to simulate six of the identified quadrants with a data resolution of type 1:1 (each cell contains the information of only one cell).

Once the three-dimensional geometric modeling phase was completed in Rhinoceros, it was necessary to transfer this model to the parametric Grasshopper environment by associating each closed geometry in Rhinoceros with a corresponding “Closed Brep” in Grasshopper (no further add-ons are required to carry out this operation).

The modeling phase is completed with the ‘transformation’ of the 3D model into a model that can be read for microclimate simulation. This is accomplished by using the tools provided by Grasshopper plug-ins: Dragonfly and df_envimet (2022 version) [35]. These software plug-ins allow the association of the physical, thermal, and environmental characteristics required to simulate the microclimatic behavior of the area to the previously realized geometries. In this step, each geometry is associated with a material from the materials database provided by ENVI-met. Once this association is completed, a model is obtained and ready to be read by the ENVI-met Suite program Spaces. The modeling phase (Figure 6) was conducted to reconstruct the building–outdoor space complex in its pre-intervention state and then, ex-post, the application of the climate-responsive design hypothesis.

2.4.4. Simulation Phase

The first step in the simulation phase concerns loading the climate data for the study area. To do this, it is necessary to fit the Ladybug plug-in into the generative algorithm—developed in Grasshopper—which allows the climate data contained in the .epw files to be imported into the process. EnergyPlus Weather (EPW) is a file format developed by EnergyPlus, which has been adopted as a standard file format for climate data by numerous building simulation tools [36]. The file records climate data (weather data) recorded by weather stations throughout the year. In the study case, data from the Naples-Capodichino weather station are imported into the generative algorithm and relate to the year 2005, the latest available data recorded to date. It was decided not to further project the climate data in order not to affect the accuracy of the simulation output. The .epw files correspond to the .stat files (expanded EnergyPlus weather statistics), which report statistics on the climate data collected in the EPW that are useful for setting the basic conditions for the simulation.

In fact, through Ladybug, a series of basic climatic conditions (such as wind direction, humidity, and temperature) and the geographical coordinates are directly imported from the .epw file. Nevertheless, through the .stat file associated with the .epw file, the specific day on which the simulation is to be carried out is directly imported. In this case, it was chosen to finalize the day on which the worst conditions occur during Extreme Hot Week.

After setting the necessary climatic information, it is associated with the previously realized model, complete with all its physical, thermal, and environmental characteristics. The following step is to choose the period over which to carry out the simulation (24 h) and then start the simulation using the ‘Simple Forcing’ option for temperature and humidity, as also suggested in the literature on the subject to resolve the problems relating to lateral boundaries recorded in previous versions of the software. The simulation process starts directly from Grasshopper’s parametric environment using, however, Envi-met Core, from the ENVI-met suite.

As with the modeling phase, the simulation phase was carried out ex-ante and ex-post for the application of the climate-proof intervention categories for the former Centro Polifunzionale Marianella.

2.4.5. Software Interoperability

One significant challenge in the workflow revolves around the interoperability process, which is heavily reliant on employing suitable IT tools. By considering the interoperability of their outputs, these results become easily interpretable and translatable across various software platforms (Figure 7). This ensures seamless monitoring across different software environments, including geometric modeling with Rhinoceros, parametric design utilizing Grasshopper and its add-ons, and microclimate simulation using ENVI-met 5.

Each piece of software requires the joint use of different tools, with the need to export and import different files, repeating the process several times, until the final definition of the definitive operational process is obtained.

Interoperability was possible above all by the use of Grasshopper’s VPL, which made it possible, through the development of a generative algorithm, bringing together different basic components of Grasshopper, Ladybug, Dragonfly, and df_envimet, to pass data from the basic geometric model created in Rhinoceros and the different ENVI-met 5 suites for microclimate simulation.

2.4.6. Simulation and Data Extraction

After the simulation phase is complete, the ENVI-met 5 software produces a folder containing the outputs of the microclimate simulations for each model processed. Since no specific time intervals (time steps) were set, the simulation process generated hourly outputs. To evaluate the effectiveness of the climate adaptation measures, an analysis of the outputs was carried out at 12 a.m.

To analyze the data produced by the simulation phase, ‘Leonardo’, an additional application of the ENVI-met 5 software, was used. This suite provides a basic visualization in the form of a map. The data are processed by the software and, based on the previously created model, returns a two-dimensional representation of the study area. Each cell on the map is associated with a color representing the value of a specific parameter being analyzed. In addition, the data can be exported in .csv format to allow further statistical analysis using data visualization and analysis software such as Excel (Office version 365).

In the specific case study, it was decided to use both methods of collecting and representing the data resulting from the simulations, both for the ‘synthetic’ model covering the entire study area and for the detailed models of individual quadrants. For each environmental parameter analyzed (such as PMV, mean radiant temperature, potential air temperature, surface temperature, and CO₂ concentration), a color scale was defined in which each color corresponds to a specific parameter value (Figure 8). As a result, the cartographic representations of the outputs are clear and concise tools, facilitating decision making in urban design phases.